A Data Platform for the future
Redpill Linpro enable Data Scientists and data professionals in their work to create data driven organisations and deliver fine tuned data analytics based on real life information. We let Data Scientists focus on challenges that are unique to your business and not on infrastructure or data pipelines. We do this by setting you up with a Modern Cloud Data Platform, giving your organisation access to seamless end to end data analytics.
Data comes in different formats, speed, quality and volumes of it. To be able to extract business value from all this you need a platform that is flexible enough to handle all your data, both present and in the future.
One size does not fit all, but we can help guide you in this transition all the way from planning to migrating and also managing your pipelines. You can start small, step by step and think big, or do a larger move all at once, all depending on your needs.
The Databricks Lakehouse platform supports all sides of analytics and is maybe the fastest growing platform today. The platform is based on Open Source tools and resides on either AWS, Azure or Google Cloud (GCP).
The Databricks Lakehouse solution combines the best elements of data lakes and data warehouses. There is a growing convergence in general between traditional BI analytics and Data Science. The Lakehouse framework sits right in its sweet spot. Combined you get the data management and performance found in data warehouses, but with the cheaper and more flexible object-type storage offered by data lakes.
We can set you up with an AWS S3 Data Lake with the Databricks Delta solution on top to create a Lakehouse. This allows for low cost storage at the same time as managing any type of data, both structured, semi-structured and unstructured data. The pipelines we set up will handle both batch processed data and streaming or event data all at once. Combined with our expertise within API & Integration Redpill Linpro is also in a unique position to help with collection of the required data for analytics.
When migrating over to a Databricks platform, we will work with you and help you organize your data either tailor made or by using a reference architecture such as the Databricks standard three layer storage:
- Raw data – managed and catalogued for easy query
- Historical data – enables time travelling for your Machine Learning model training
- Aggregated – typically BI data to support the Data Warehouse
You can query all data that resides in this Lakehouse in any of the above layers, whether it is for predictive or descriptive analysis. In other words, supporting your BI analyst, Data Scientist and Machine learning processors.
Enabling the Data Scientist also applies to the process after having made all data available in a useable format. We want the Data Scientist to focus on their ML code, not the infrastructure surrounding it and how to deploy it to production.
The Databricks platform facilitates and streamlines the end-to-end data science workflow giving your Data Scientist a highly collaborative environment that supports several languages, versioning, notebooks and last but no least, the MLOps service Mlflow.
Mlflow in short provides an easy way of achieving MLOps - DevOps for Machine learning. Your Data Scientist will be able to deploy their models directly to production from their own model registry. We can set up pipelines or even tailor the CI/CD process fit to need and Databricks will ensure end points for the applications to query.
Contact us for more information on how we can assist your organisation with access to seamless end to end data analytics.